Overcoming Exploration in Reinforcement Learning with Demonstrations

Adam: A Method for Stochastic Optimization

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Human-level control through deep reinforcement learning

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Neural Machine Translation by Jointly Learning to Align and Translate

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Mastering the game of Go with deep neural networks and tree search

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Asynchronous Methods for Deep Reinforcement Learning

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Probabilistic roadmaps for path planning in high-dimensional configuration spaces

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MuJoCo: A physics engine for model-based control

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Trust region policy optimization

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End to End Learning for Self-Driving Cars

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MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization

Niu, H., Li, S., & Li, J. (2022) · International Conference on Information and Knowledge Management · doi:10.1145/3511808.3557363 · Cited by 59

Real-time artificial intelligence for accelerator control: A study at the Fermilab Booster

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Visuospatial foresight for physical sequential fabric manipulation

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Disagreement-Regularized Imitation Learning

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Imitation Learning with Concurrent Actions in 3D Games

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Learning to combine primitive skills: A step towards versatile robotic manipulation §

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Relay Hindsight Experience Replay: Self-guided continual reinforcement learning for sequential object manipulation tasks with sparse rewards

Luo, Y., Wang, Y., Dong, K., Zhang, Q., Cheng, E., Sun, Z., & Song, B. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126620 · Cited by 46

Certified reinforcement learning with logic guidance

Hasanbeig, H., Kroening, D., & Abate, A. (2023) · Artificial Intelligence · doi:10.1016/j.artint.2023.103949 · Cited by 67

Learning Deep Visuomotor Policies for Dexterous Hand Manipulation

Jain, D., Li, A. R., Singhal, S., Rajeswaran, A., Kumar, V., & Todorov, E. V. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794033 · Cited by 49

Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning

Qiao, Z., Tyree, Z., Mudalige, P., Schneider, J., & Dolan, J. M. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341496 · Cited by 45

Learning and Adapting Agile Locomotion Skills by Transferring Experience

Smith, L. E., Kew, J. C., Li, T., Luu, L., Peng, X. B., Ha, S., Tan, J., & Levine, S. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.051 · Cited by 60

Artificial intelligence in the autonomous navigation of endovascular interventions: a systematic review

Robertshaw, H. H., Karstensen, L., Jackson, B. M., Sadati, S. M. H., Rhode, K., Sebastien Ourselin, A., Granados, A., & Booth, T. C. (2023) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1239374 · Cited by 47

A Systematic Literature Review on AI Safety: Identifying Trends, Challenges, and Future Directions

Salhab, W., Ameyed, D., Jaafar, F., & Mcheick, H. (2024) · IEEE Access · doi:10.1109/access.2024.3440647 · Cited by 50

Data-Centric and Model-Centric AI: Twin Drivers of Compact and Robust Industry 4.0 Solutions

Hamid, O. H. (2023) · Applied Sciences · doi:10.3390/app13052753 · Cited by 46

C·ASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters

Dou, Z., Chen, X., Fan, Q., Komura, T., & Wang, W. (2023) · SIGGRAPH Asia Conference Papers · doi:10.1145/3610548.3618205 · Cited by 44

Learning Self-Imitating Diverse PoliciesPreprint

Gangwani, T., Liu, Q., & Peng, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10309 · Cited by 56

Deep Generative Adversarial Reinforcement Learning for Semi-Supervised Segmentation of Low-Contrast and Small Objects in Medical Images

Xu, C., Zhang, T., Zhang, D., Zhang, D., & Han, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3383716 · Cited by 39

Truncated Horizon Policy Search: Combining Reinforcement Learning & Imitation LearningPreprint

Sun, W. Y., Bagnell, J. A., & Boots, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11240 · Cited by 61

A Collaborative Control Method of Dual-Arm Robots Based on Deep Reinforcement Learning

Liu, L., Liu, Q., Song, Y. H., Pang, B. J., Yuan, X., & Xu, Q. (2021) · Applied Sciences · doi:10.3390/app11041816 · Cited by 41

Complex Robotic Manipulation via Graph-Based Hindsight Goal Generation

Bing, Z., Brucker, M., Morin, F. O., Li, R., Su, X., Huang, K., & Knoll, A. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3088947 · Cited by 37

Learning Deformable Object Manipulation From Expert Demonstrations

Salhotra, G., Liu, I.-C. A., Dominguez-Kuhne, M., & Sukhatme, G. S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3187843 · Cited by 37

Deep Reinforcement Learning for the Navigation of Neurovascular Catheters

Behr, T., Pusch, T., Siegfarth, M., Hüsener, D., Mörschel, T., & Karstensen, L. (2019) · Current Directions in Biomedical Engineering · doi:10.1515/cdbme-2019-0002 · Cited by 37

Sample-efficient Reinforcement Learning in Robotic Table Tennis

Tebbe, J., Krauch, L., Gao, Y., & Zell, A. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560764 · Cited by 39

Reinforcement learning for robotic manipulation using simulated locomotion demonstrations

Kilinc, O., & Montana, G. (2021) · Machine Learning · doi:10.1007/s10994-021-06116-1 · Cited by 39

Visual Tracking by Means of Deep Reinforcement Learning and an Expert Demonstrator

Dunnhofer, M., Martinel, N., Foresti, G. L., & Micheloni, C. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00282 · Cited by 35

Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning

Hou, Z., Jin, X., Li, Z., & Bai, L. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.412 · Cited by 35

Learning Task-Parameterized Skills From Few Demonstrations

Zhu, J., Gienger, M., & Kober, J. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3150013 · Cited by 34

Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot

Huang, T., Chen, K., Li, B., Liu, Y., & Dou, Q. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160327 · Cited by 34

Autonomous target tracking of multi-UAV: A two-stage deep reinforcement learning approach with expert experience

Wang, J., Zhang, P., & Wang, Y. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110604 · Cited by 35

UAV Swarm Cooperative Dynamic Target Search: A MAPPO-Based Discrete Optimal Control Method

Wei, D., Zhang, L., Liu, Q., Chen, H. P., & Huang, J. (2024) · Drones · doi:10.3390/drones8060214 · Cited by 39

Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks

Marzari, L., Pore, A., Dall’Alba, D., Aragón-Camarasa, G., Farinelli, A., & Fiorini, P. (2021) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar53236.2021.9659344 · Cited by 34

Integrated Learning-Based Framework for Autonomous Quadrotor UAV Landing on a Collaborative Moving UGV

Wang, C., Wang, J., Ma, Z., Xu, M., Qi, K., Ji, Z., & Wei, C. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3425755 · Cited by 33

Rate-Splitting for Intelligent Reflecting Surface-Aided Multiuser VR Streaming

Huang, R., Wong, V. W. S., & Schober, R. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3240710 · Cited by 33

Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments From Temporal Logic Specifications

Cai, M., Aasi, E., Belta, C. A., & Vasile, C.-I. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3246844 · Cited by 32

Efficient learning of power grid voltage control strategies via model-based deep reinforcement learning

Hossain, R. R., Yin, T., Du, Y., Huang, R., Tan, J., Yu, W., Liu, Y., & Huang, Q. (2023) · Machine Learning · doi:10.1007/s10994-023-06422-w · Cited by 32

Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning

Ota, K., Jha, D. K., Oiki, T., Miura, M., Nammoto, T., Nikovski, D. N., & Mariyama, T. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968010 · Cited by 32

Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks With Base Controllers

Wang, G., Xin, M., Wu, W., Liu, Z., & Wang, H. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3201705 · Cited by 32

A deep multi-agent reinforcement learning framework for autonomous aerial navigation to grasping points on loads

Chen, J., Ma, R., & Oyekan, J. O. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104489 · Cited by 31

Introducing PetriRL: An innovative framework for JSSP resolution integrating Petri nets and event-based reinforcement learning

Lassoued, S., & Schwung, A. (2024) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2024.04.028 · Cited by 31

Imaginary filtered hindsight experience replay for UAV tracking dynamic targets in large-scale unknown environments

Hu, Z., Gao, X., Wan, K., Neretin, E. S., & Li, J. (2022) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2022.09.008 · Cited by 31

Multiagent Deep Reinforcement Learning With Demonstration Cloning for Target Localization

Alagha, A., Mizouni, R., Bentahar, J., Otrok, H., & Singh, S. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3262663 · Cited by 31

Hybrid Trajectory and Force Learning of Complex Assembly Tasks: A Combined Learning Framework

Wang, Y., Beltran-Hernandez, C. C., Wan, W., & Harada, K. (2021) · IEEE Access · doi:10.1109/access.2021.3073711 · Cited by 30

An Adaptive Imitation Learning Framework for Robotic Complex Contact-Rich Insertion Tasks

Wang, Y., Beltran-Hernandez, C. C., Wan, W., & Harada, K. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.777363 · Cited by 30

RLingua: Improving Reinforcement Learning Sample Efficiency in Robotic Manipulations With Large Language Models

Chen, L., Lei, Y., Jin, S., Zhang, Y., & Zhang, L. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3400189 · Cited by 35

Few-Shot Bayesian Imitation Learning with Logical Program Policies

Silver, T., Allen, K. R., Lew, A. K., Kaelbling, L. P., & Tenenbaum, J. B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i06.6587 · Cited by 30

A Framework for Learning From Demonstration With Minimal Human Effort

Rigter, M. P., Lacerda, B., & Hawes, N. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2970619 · Cited by 29

Improving Sample Efficiency of Multiagent Reinforcement Learning With Nonexpert Policy for Flocking Control

Qiu, Y., Jin, Y., Yu, L., Wang, J., Wang, Y., & Zhang, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3240671 · Cited by 29

Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous DrivingPreprint

Wu, J., Huang, Z., Huang, C., Hu, Z., Hang, P., Xing, Y., & Lv, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.07246 · Cited by 46

Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning

Qiao, Z., Schneider, J., & Dolan, J. M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561095 · Cited by 32

Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation

Zhan, A., Zhao, R., Pinto, L., Abbeel, P., & Laskin, M. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981055 · Cited by 58

Reinforcement Learning with Videos: Combining Offline Observations with InteractionPreprint

Schmeckpeper, K., Rybkin, O., Daniilidis, K., Levine, S., & Finn, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.06507 · Cited by 57

Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force Learning

Wang, Y., Beltran-Hernandez, C. C., Wan, W., & Harada, K. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561619 · Cited by 27

Reinforcement learning in robotic motion planning by combined experience-based planning and self-imitation learning

Luo, S., & Schomaker, L. (2023) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2023.104545 · Cited by 27

Adaptive Hybrid Optimization Learning-Based Accurate Motion Planning of Multi-Joint Arm

Bai, C., Zhang, J., Guo, J., & Yue, C. P. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3262109 · Cited by 26

Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation

Khandate, G., Shang, S., Chang, E. T., Saidi, T. L., Adams, J., & Ciocarlie, M. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.020 · Cited by 26

UGV Navigation Optimization Aided by Reinforcement Learning-Based Path Tracking

Wei, M., Wang, S., Zheng, J., & Chen, D. (2018) · IEEE Access · doi:10.1109/access.2018.2872751 · Cited by 27

CURIOUS: Intrinsically Motivated Multi-Task Multi-Goal Reinforcement Learning

Colas, C., Fournier, P., Sigaud, O., & Oudeyer, P. (2018) · HAL AMU · Cited by 25

Investigating exploration for deep reinforcement learning of concentric tube robot control

Iyengar, K., Dwyer, G., & Stoyanov, D. V. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02194-z · Cited by 27

A conceptual framework for externally-influenced agents: an assisted reinforcement learning review

Bignold, A., Cruz, F., Taylor, M. E., Brys, T., Dazeley, R., Vamplew, P., & Foale, C. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03489-y · Cited by 24

Crash mitigation controller for unavoidable T-bone collisions using reinforcement learning

Hou, X., Zhang, J., He, C., Li, C., Ji, Y., & Han, J. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.03.021 · Cited by 24

Learning Skills From Demonstrations: A Trend From Motion Primitives to Experience Abstraction

Tavassoli, M., Katyara, S., Pozzi, M., Deshpande, N., Caldwell, D. G., & Prattichizzo, D. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3296166 · Cited by 24

Autonomous Reinforcement Learning of Multiple Interrelated Tasks

Santucci, V. G., Baldassarre, G., & Cartoni, E. (2019) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2019.8850713 · Cited by 24

Hierarchical Deep Q-Network from imperfect demonstrations in Minecraft

Skrynnik, A., Staroverov, A., Aitygulov, E., Aksenov, K., Davydov, V., & Panov, A. I. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2020.08.012 · Cited by 24

Federated Reinforcement Learning for smart and privacy-preserving energy management of residential microgrids clusters

Tan, M., Zhao, J., Liu, X., Su, Y., Wang, L., Wang, R., & Dai, Z. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109579 · Cited by 23

Secondary crash mitigation controller after rear-end collisions using reinforcement learning

Hou, X., Gan, M., Zhang, J., Zhao, S., & Ji, Y. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102176 · Cited by 23

RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration

Pavse, B. S., Torabi, F., Hanna, J. P., Warnell, G., & Stone, P. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3010750 · Cited by 25

Accelerated Robot Learning via Human Brain Signals

Akinola, I., Wang, Z., Shi, J., He, X., Lapborisuth, P., Xu, J., Watkins-Valls, D., Sajda, P., & Allen, P. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196566 · Cited by 23

DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance

Tan, Q., Fan, T., Pan, J., & Manocha, D. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341805 · Cited by 23

Backplay: "Man muss immer umkehren"Preprint

Resnick, C., Răileanu, R., Kapoor, S., Peysakhovich, A., Cho, K., & Bruna, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.06919 · Cited by 47

Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration DataPreprint

He, L., Aouf, N., Whidborne, J. F., & Song, B. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02521 · Cited by 23

Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control

Hermann, L., Argus, M. J., Eitel, A., Amiranashvili, A., Burgard, W., & Brox, T. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197108 · Cited by 23

Learning synergies based in‐hand manipulation with reward shaping

Deng, Z., & Zhang, J. W. (2020) · CAAI Transactions on Intelligence Technology · doi:10.1049/trit.2019.0094 · Cited by 23

Knowledge-Guided Reinforcement Learning Control for Robotic Lower Limb Prosthesis

Gao, X. H., Si, J., Wen, Y., Li, M., & Huang, H. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196749 · Cited by 22

Planning-Augmented Hierarchical Reinforcement Learning

Gieselmann, R., & Pokorny, F. T. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3071062 · Cited by 23

Hierarchical Policies for Cluttered-Scene Grasping With Latent Plans

Wang, L., Meng, X., Yu, X., & Fox, D. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3143198 · Cited by 22

Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning

Zhang, Z., Hong, J., Enayati, A. M. S., & Najjaran, H. (2024) · IEEE Transactions on Robotics · doi:10.1109/tro.2024.3468770 · Cited by 21

Natural object manipulation using anthropomorphic robotic hand through deep reinforcement learning and deep grasping probability network

Añazco, E. V., Lopez, P. R., Park, N., Oh, J.-H., Ryu, G., Al–antari, M. A., & Kim, T. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01870-6 · Cited by 21

Improving exploration efficiency of deep reinforcement learning through samples produced by generative model

Xu, D., Zhu, F., Liu, Q., & Zhao, P. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115680 · Cited by 21

Outperforming the tutor: Expert-infused deep reinforcement learning for dynamic portfolio selection of diverse assets

Choi, C.-W., & Kim, J. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111739 · Cited by 21

Learning from demonstration for autonomous generation of robotic trajectory: Status quo and forward-looking overview

Li, W., Wang, Y., Liang, Y., & Pham, D. T. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102625 · Cited by 21

Integration of imitation learning using GAIL and reinforcement learning using task-achievement rewards via probabilistic graphical model

Kinose, A., & Taniguchi, T. (2020) · Advanced Robotics · doi:10.1080/01691864.2020.1778521 · Cited by 22

Learning Precise 3D Manipulation from Multiple Uncalibrated Cameras

Akinola, I., Varley, J., & Kalashnikov, D. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197181 · Cited by 21

NPE-DRL: Enhancing Perception Constrained Obstacle Avoidance With Nonexpert Policy Guided Reinforcement Learning

Zhang, Y., Yan, C., Xiao, J., & Feroskhan, M. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3464510 · Cited by 20

Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning

Yang, Z., Nguyen, L., Zhu, J., Pan, Z., Li, J., & Jin, F. (2020) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam49781.2020.9381416 · Cited by 20

Meta-Residual Policy Learning: Zero-Trial Robot Skill Adaptation via Knowledge Fusion

Peng, H., Lu, T., Cui, S., Wei, J., Cai, Y., & Wang, S. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3146916 · Cited by 20

AMBF-RL: A real-time simulation based Reinforcement Learning toolkit for Medical Robotics

Varier, V. M., Rajamani, D. K., Tavakkolmoghaddam, F., Munawar, A., & Fischer, G. S. (2022) · International Symposium on Medical Robotics · doi:10.1109/ismr48347.2022.9807609 · Cited by 20

Reinforcement learning from expert demonstrations with application to redundant robot control

Ramírez, J., & Yu, W. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105753 · Cited by 22

Recent Advances of Deep Robotic Affordance Learning: A Reinforcement Learning Perspective

Yang, X., Ji, Z., Wu, J., & Lai, Y. (2023) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2023.3277288 · Cited by 24

Subtask-masked curriculum learning for reinforcement learning with application to UAV maneuver decision-making

Hou, Y., Liang, X., Lv, M., Yang, Q., & Li, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106703 · Cited by 21

A Q-learning approach to the continuous control problem of robot inverted pendulum balancing

Safeea, M., & Neto, P. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200313 · Cited by 20

Accelerating Reinforcement Learning for Autonomous Driving Using Task-Agnostic and Ego-Centric Motion Skills

Zhou, T., Wang, L., Chen, R., Wang, W., & Liu, Y. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341449 · Cited by 22

Improved Cooperative Multi-agent Reinforcement Learning Algorithm Augmented by Mixing Demonstrations from Centralized Policy

Lee, H.-R., & Lee, T. (2019) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/hxwh4898 · Cited by 19

Hybrid Reinforcement Learning with Expert State Sequences

Guo, X., Chang, S., Yu, M., Tesauro, G. J., & Campbell, M. S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013739 · Cited by 23

PBCS: Efficient Exploration and Exploitation Using a Synergy Between Reinforcement Learning and Motion Planning

Matheron, G., Perrin, N., & Sigaud, O. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61616-8_24 · Cited by 21

Augmenting GAIL with BC for sample efficient imitation learningPreprint

Jena, R., Liu, C., & Sycara, K. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.07798 · Cited by 21

Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects

Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 27

Learning contact-rich whole-body manipulation with example-guided reinforcement learning

Barreiros, J. A. L., Önol, A. Ö., Zhang, M., Creasey, S., Goncalves, A., Beaulieu, A., Bhat, A. D., Tsui, K., & Alspach, A. N. (2025) · Science Robotics · doi:10.1126/scirobotics.ads6790 · Cited by 22

Reward Learning From Very Few Demonstrations

Eteke, C., Kebüde, D., & Akgün, B. (2020) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3038698 · Cited by 19

An Inverse Kinematics Solution for a Series-Parallel Hybrid Banana-Harvesting Robot Based on Deep Reinforcement Learning

Lin, G., Huang, P., Wang, M., Xu, Y., Zhang, R., & Zhu, L. (2022) · Agronomy · doi:10.3390/agronomy12092157 · Cited by 19

Efficient Lane-changing Behavior Planning via Reinforcement Learning with Imitation Learning Initialization

Shi, J., Zhang, T., Zhan, J., Chen, S., Xin, J., & Zheng, N. (2023) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55152.2023.10186577 · Cited by 19

Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., & Riedmiller, M. (2017) · arXiv · doi:10.48550/arxiv.1707.08817 · Cited by 478

Efficient hindsight reinforcement learning using demonstrations for robotic tasks with sparse rewards

Zuo, G., Zhao, Q., Lu, J., & Li, J. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881419898342 · Cited by 22

Learning from Demonstration without Demonstrations

Blau, T., Morere, P., & Francis, G. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561119 · Cited by 3

Abstract Demonstrations and Adaptive Exploration for Efficient and Stable Multi-step Sparse Reward Reinforcement Learning

Yang, X., Ji, Z., Wu, J., & Lai, Y. (2022) · International Conference on Automation and Computing (ICAC) · doi:10.1109/icac55051.2022.9911100 · Cited by 6

Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model LearningPreprint

Escoriza, A. L., Hansen, N., Tao, S., Mu, T., & Su, H. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2503.01837 · Cited by 6